Ensemble of naive Bayes, decision tree, and random forest to predict air quality - Resti, Yulia; Eliyati, Ning; Rahmayani, Mau’izatil; Alwine Zayanti, Des; Sri Kresnawati, Endang; Setyo Cahyono, Endro; Yani, Irsyadi (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
Air quality prediction is an important research issue because air quality can affect many areas of life. This study aims to predict air quality using the ensemble method and compare the results with the prediction results using a single method. The proposed ensemble method is built from three singlesupervised methods: naïve Bayes, decision trees, and random forests. The results show that the ensemble method performs better than the single methods. The ensemble method achieves the highest performance with scores of 99.89% accuracy, 79.6% precision, 79.81% recall, and 79.7% F1-score. The performance comparison between single and ensemble models is expected to provide information on the percentage increase in predictive model performance metrics from the single to ensemble methods.